- What is the “Institutional Model”? What prompted the deinstitutionalization movement?
- Explain the assumptions underlying both the developmental and ecological models. What is the difference between both of these models? Which model achieved better outcomes?
Traveling Salesman fitness results compared to optimization algorithm iterations. Flip Flop Problem – Simulated Annealing. The Flip Flop problem is, by far, the simplest optimization problem used throughout our analysis. At its core, Flip Flop involves a rudimentary fitness function which looks to find the total number of consecutive bit alternations within a bit string. In other words, while a bit string of ‘000’ would score 0, a bit string of ‘101’ would score 2. As the optimal configuration of bits within a bit string of length N would consist of continuously alternating bits, the global optima of such a problem would be exactly N – 1. Our goal is to determine which optimization algorithm performs best on this problem. Like before, we ran two experiments; one to observe an algorithm’s ability to scale to larger search spaces (increasing the size of the bit string), and another to determine optimization efficiency. We utilized ABAGAIL’s default hyperparameters in our testing, which can be seen in Table 5. SA Starting Temp Cooling Factor 100 0.95 GA Pop. Size # to Mate # to Mutate 200 100 20 MIMIC Sample Count # to Keep 200 5 Table 5. Optimization algorithm hyperparameters, pulled from ABAGAIL’s Flip Flop testing implementation. Randomized Hill Climbing not listed, as no hyperparameters are applicable. For the complexity test, we ran five experiments per algorithm, comparing N values (from 100 to 500, steps of 100) to resulting accuracies (Figure 7). Each algorithm was allowed exactly 2 seconds to run. It is immediately evident that Simulated Annealing scales the most effectively; aside from its curve staying remarkably flat throughout the bit count increases, no other algorithm comes close to its accuracy – remaining near 100% throughout the experiment. It’s apparent that Simulated Annealing (and Randomized Hill Climbing too) scale well with the size of a problem’s search space. Alternatively, the Genetic Algorithm a>GET ANSWER